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inference.py
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inference.py
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from conf import conf
from pro_data import dataloader
from pro_data import dataloader1
from Model import ADMN3
from Model import ADMN2
from Model import ADMN
from Model import ADMN5
import pickle
import time
import os
import json
def save_data(data,parameter):
now = time.strftime('%Y%m%d_%H%M%S', time.localtime(time.time()))
save_path = os.path.join(os.getcwd(), "Result")
save_file = os.path.join(save_path, str(parameter["data"]) ,now + "_result.txt")
output = open(save_file, 'w+', encoding="utf-8")
output.write('best_mse:' + str(min(data))+'\n')
for i in data:
output.writelines(str(i)+'\n')
output.writelines("-----------------------------------"+'\n')
output.writelines("parameter"+'\n')
for key, item in parameter.items():
lines = str(key) + ":" + str(item)
output.writelines(lines + '\n')
output.writelines(str(type(model)))
output.close()
def load_item2id():
#返回id对应的item名字
with open(os.path.join(path,parameter["data"]+'_item2id'),'rb') as f:
item2id = dict(pickle.load(f))
id2item = {}
for key,item in item2id.items():
id2item[item] = key
return id2item
def save_rec_item():
with open(os.path.join(path,parameter["data"]+"_user_rec1"), 'rb') as f:
user_rec_item = dict(pickle.load(f))
id2item =load_item2id()
rec_item_dict = {}
for key,item in user_rec_item.items():
list = []
for i in item:
#print(id2item[i])
list.append(id2item[i])
rec_item_dict[key] = list
pickle.dump(rec_item_dict,open(os.path.join(path,parameter["data"]+"_user_rec_item1"),'wb+'))
def print_user_rec_item(user_id):
with open(os.path.join(path,parameter["data"]+"_user_rec_item"),'rb') as f:
user_rec_item = dict(pickle.load(f))
rec_item = user_rec_item[user_id]
print(rec_item)
if __name__=="__main__":
a = conf.args()
#print(b["batch_size"])
parameter = a.args
current_path =os.path.join(os.getcwd(),"Data")
print(current_path)
path = os.path.join(current_path,parameter["data"]) #
file_name = parameter["data"] + ".json"
datahelper = dataloader1.dataloader(path,file_name,parameter)
#datahelper.load_file() #数据处理完就不用处理
#datahelper.process_data()
#下面为了免去重复数据分析
parameter["vocabulary_num"] = datahelper.args["vocabulary_num"] #548680
parameter["user_num"] = datahelper.args["user_num"] #5541
parameter["item_num"] = datahelper.args["item_num"] #3568
parameter['is_sample'] = False
parameter['train_time'] = 2
#model = ADMN.ADMN(parameter)
#print(type(model))
pkl_file =os.path.join(current_path,parameter["data"] ,parameter["data"] + '.para')
train_path = os.path.join(current_path,parameter["data"],parameter["data"]+'.train')
test_path = os.path.join(current_path,parameter["data"],parameter["data"]+'.test')
#model.load_data(train_path,test_path,pkl_file)
#list = ['base','softmax','unbias_softmax','abs_unbias','abs_unbias_softmax','no_rating']
model = ADMN5.ADMN(parameter)
print(type(model))
model.load_data(train_path, test_path, pkl_file)
model.model_train()
model.show_test_result()
user_rec = dict() #保存所有用户的推荐商品
for i in range(parameter["user_num"]):
result_rec = list(model.inference(i))
user_rec[i] = result_rec
if i%100==0 and i>0:
print("user_id:{}".format(i))
pickle.dump(user_rec,open(os.path.join(path,parameter["data"]+"_user_rec1"),"wb+"))
save_rec_item() #将id转化为item名字，并保存起来
#print_user_rec_item(1) #输入用户id，返回推荐商品
#print_user_rec_item(4)
#pickle.dump(self.item2id, open(os.path.join(self.TPS_DIR, self.args["data"] + '_item2id'), "wb+"))
#pickle.dump(self.user2id, open(os.path.join(self.TPS_DIR, self.args["data"] + '_user2id'), "wb+"))
#model.model_train()
#model.show_test_result()
#result = model.test_loss_list
#save_data(result, parameter)
# model.model_train()
# model.show_test_result()
# result = model.test_loss_list
# save_data(result,parameter)